The Metadata Powerwash - Integrated tools to make biomedical data FAIR

项目摘要 描述科学数据的元数据是实现(1) 数据的发现和再利用以及(2)产生数据的实验的可重复性 数据放在首位。元数据对于科学家理解相关数据至关重要 并重复使用它们，以及信息技术对数据进行索引，使数据 可用，并为科学家搜索相应的数据集提供过滤器。 目前，公共存储库中托管的科学元数据存在多个质量问题 这限制了科学家发现和重复使用他们所指的实验数据集的能力。它可以 花费科学家数周的时间来确定满足特定要求的数据集的集合 当数据描述得如此之差时的标准-并且过程的大部分必然 手册。 我们建议开发一个端到端的解决方案来标准化生物医学元数据 本体的帮助-定义应用程序域中的术语的数据结构 他们之间的关系。有数百种本体论为以下内容提供标准术语 在生物医学中使用，它们是制作生物医学元数据的必要资源 可互操作和可重复使用。我们的方法还将建立在 扩展数据注释和检索中心(Cedar)，该中心提供 用于定义基于计算机的元数据模板的块和公共数据元素 社区标准。 我们的计划涉及三个具体目标。首先，我们将开发一种方法和工具来标准化 可能出现在元数据中以表示相同的多个即席元数据字段名称 通过将这些字段名替换为标准中使用的字段名来确定信息类型 元数据模板，或者，如果没有合适的模板匹配，则使用相关 本体论。其次，我们将开发方法和工具来标准化不同类型的元数据 字段值，例如，药品或疾病等类型值和数值 例如年龄或样本采集日期。第三，我们将评估速度、精确度和召回率 我们的元数据转换管道-由标准化字段的方法和工具构建而成 名称和值-基于我们将手动管理的大型元数据语料库 现有公共元数据。我们还将进行实验，以测试 当生物医学科学家在其上下文中执行数据集搜索时标准化的元数据 工作。

Project Summary The metadata that describe scientific data are fundamental resources to enable (1) the discovery and reuse of the data and (2) the reproducibility of the experiments that generated the data in the first place. Metadata are essential for scientists to understand the associated data and to reuse them, as well as for information technology to index the data, to make the data available, and to provide filters for scientists to search for the corresponding datasets. Currently, the scientific metadata hosted in public repositories suffer from multiple quality issues that limit scientists’ ability to find and reuse the experimental datasets to which they refer. It can take many weeks of a scientist’s time to identify a collection of datasets that fulfill specific criteria when the data are so poorly described—and the majority of the process is necessarily manual. We propose to develop an end-to-end solution to standardize biomedical metadata with the help of ontologies—data structures that define the terms in an application domain and the relationships among them. There are hundreds of ontologies that provide standard terms for use in biomedicine, and they are essential resources to make biomedical metadata interoperable and reusable. Our approach also will build on the technology created by the Center for Expanded Data Annotation and Retrieval (CEDAR), which offers a library of building blocks and common data elements for defining computer-based metadata templates based on community standards. Our plan involves three specific aims. First, we will develop a method and tool to standardize the multiple, ad hoc metadata field names that may appear in metadata to represent the same type of information by replacing those field names with the field names used in standard metadata templates or, if no appropriate template match is available, with terms from a relevant ontology. Second, we will develop methods and tools to standardize different types of metadata field values, for example, categorical values such as drugs or diseases, and numerical values such as age, or sample collection date. Third, we will evaluate the speed, precision, and recall of our metadata transformation pipeline—built out of the methods and tools to standardize field names and values—on a large corpus of metadata that we will manually curate based on existing public metadata. We will also carry out experiments to test the effect of the standardized metadata when biomedical scientists perform dataset search in the context of their work.